AI-Ready Data: Characteristics, Challenges & Best Practices

Despite the increasing adoption of Artificial Intelligence (AI) applications, most organizations are bound to see implementation challenges. One of the issues lies in the data itself. A recent survey showed 80% of companies believe their data is suitable for AI, but more than half are actually dealing with challenges like internal data quality and categorization difficulties. 

This mismatch between perception and reality highlights the crucial need for a more comprehensive data evaluation before initializing AI projects.

How can you evaluate whether your data is really AI-ready? This article dives into the key characteristics of AI-ready data, the unique challenges it poses, and which industry best practices are essential to making data ready for AI purposes.

What is AI-Ready Data?

AI-ready data is clean, structured data organized in a uniform format and centrally accessible for AI systems. AI-ready data is like a high-quality fuel for your AI engine – after all, your AI models are only as good as the data that powers them, allowing for more accurate model training and actionable insights. 

But what qualifies data as truly AI-ready? 

AI-ready data should have the following critical characteristics:

• Complete and exact, with minor gaps and irregularities
• Correctly labeled and structured consistently
• Easy to access from a convenient place
• Private and secure, meeting compliance criteria
• Updated often to maintain relevancy
• Designed to capture behavioral patterns and user intent

Why AI-Ready Data Matters

AI-ready data is essential for organizations looking to accelerate and improve their AI programs. Such data comes with the following benefits:

• Reduces Data Preparation Time for Data Scientists – AI-ready data lets your data teams spend less time preparing data and more time developing and improving models.
• Enables Consistent MLOps Workflows – The consistency of historical and real-time data streams simplifies machine learning operations (MLOps). The seamless transition from model training to deployment helps create more efficient and successful AI deployments.
• Improves Model Accuracy and Reliability – High-quality, well-structured data facilitates the development of more accurate and trustworthy predictive models, thereby assisting your teams in making more informed decisions.
• Supports Regulatory and Audit Readiness – AI-ready data includes detailed metadata and lineage information that improve data governance, boosting data auditability and transparency, which are essential for explaining AI decisions to consumers and auditors.

Key Characteristics of AI-Ready Data

Schema-Enforced, Validated Inputs

AI models require specific data formats, standardized schemas, and consistent pre-processing to work properly. Standardizing inputs and outputs is critical for successful integration. Without it, teams may encounter mismatched data formats, unproductive processes, and wasted resources.

Well-designed APIs ensure that AI/ML models can produce predictions, classifications, and insights without additional bottlenecks, allowing for robust, scalable systems. Validating input data in API development ensures it meets model requirements for data types, dimensions, and ranges.

APIs may use schema enforcement technologies such as JSON Schema and OpenAPI to specify and validate input structures. These tools serve as gatekeepers, rejecting poor-quality data before it enters the model, reducing errors and ensuring system stability.

High-Quality, De-Duplicated Records

Duplicates can find their way into systems via daily workflows and business activities, from manual data entry into several siloed systems to documents going through multiple revisions and adjustments, with each version making a copy. As data volume increases, so do the amount of duplicates. 

This redundant data increases AI data storage costs, slows procedures, and can lead to errors. Teams lose time reconciling and aggregating duplicates between siloed systems, slowing down crucial workflows. Disparate duplicates make it difficult to establish a single source of truth. This has implications for reporting accuracy and data integrity.

Deduplication is the process of discovering and eliminating redundant copies of the same data. The goal is to eliminate duplicates to a single master copy, improving storage efficiency and data integrity.

To prepare your data for AI purposes, use algorithms that examine datasets for duplicates by comparing content across records. Once found, duplicate entries will be erased, leaving only one authentic copy. To preserve integrity, references to this master copy should be retained.

Versioned Data for Reproducibility

Frequent data changes are common when your project includes large amounts of data. Without data versioning in place, it quickly becomes difficult to debug a data issue or validate ML training accuracy (re-running a model over different data gives different results).

By implementing data version control, your team gets to keep track of more than just the current state of data. This, in turn, opens the doors to reproducibility, which is crucial for fast troubleshooting and validation.

Traceable Lineage Across Pipelines

Tracing and documenting the movement of data from its source to its final destination, including all transformations, aggregations, and changes, is key in AI projects. Data lineage provides a visual or programmatic representation of how data flows through various stages inside a system, ensuring transparency throughout the data’s journey.

Complex AI data ecosystems stand to benefit a lot of from data lineage:

• It allows you to track data back to its origins and transformations, making it easier to discover and resolve data quality concerns. 
• If there are differences or problems in downstream processes, lineage can help identify the primary cause, which could be faulty source data or flawed transformations.
• Data lineage gives the visibility needed to establish compliance, making it easier to respond to audits and regulatory inquiries.
• When you make changes to a data pipeline (for example, adjusting a transformation or replacing a data source), data lineage allows you to run impact analyses. 
• Understanding the lineage of data helps firms trust analytics and business intelligence. It ensures that decision-makers understand how the data was obtained and that the data they use is accurate and dependable.

Discoverable via Metadata Catalogs

AI-ready data needs to be discoverable, and one sure-fire method to achieve this is by investing in metadata catalogs, which create a systematic inventory of data assets and their accompanying information. 

Data catalogs improve overall data management by centralizing metadata and allowing for robust search and filtering. They enable users to readily locate, comprehend, and access the data they require for various applications, including data science, analytics, and business intelligence. 

Access-Controlled and Secure

AI-ready data should live in systems and processes that limit access to authorized individuals or entities, ensuring that only the appropriate people view, alter, or interact with sensitive information. This includes putting in place security mechanisms such as authentication, authorization, and other access control models to keep data safe from unwanted access, breaches, and misuse.

Practical Steps to Build AI-Ready Data Systems

Adopt Data Versioning Across the Stack

Implementing data versioning throughout your data stack means tracking and managing data changes at all stages, from raw data ingestion to derived datasets. This allows users to access historical data, understand data lineage, and maintain data quality and consistency.

Data versioning improves data quality and consistency by allowing the comparison of different versions and reverting to previous states. It also promotes reliable and reproducible results by tracking the exact data used for training models or creating reports.

Use Branching for Isolated Testing

Branching is an efficient approach to building isolated data testing environments. The idea follows the principles of code versioning—you make copies of a codebase and its related data, which allows for changes and testing without affecting the main or production environment. This method enables parallel development and early testing and reduces the danger of disturbing production data.

When a feature or bug repair needs testing, a data practitioner can create a new branch from the main or production branch. They can modify data and code in this isolated branch without affecting other branches or the overall environment. Once tested, the branch can be merged back into the main or production branch to incorporate the verified changes.

Track Changes and Pipeline Inputs/Outputs

Tracking pipeline changes and controlling inputs/outputs are critical for ensuring data integrity and consistency. Pipelines, particularly in ML applications, benefit from version control that tracks changes applied to both models and datasets. 

Teams should use version control systems to monitor changes to pipeline code and configurations. This lets them revert to prior versions as needed and track the evolution of your process. Implementing logging in pipeline steps is also key: this is how you monitor execution progress and identify probable issues. 

Automate Validation and Compliance Policies

To meet regulatory and policy obligations more efficiently, it’s smart to automate compliance-related operations such as monitoring, data gathering, policy enforcement, and reporting. This eliminates manual labor, improves accuracy, and guarantees that the criteria are met consistently.

Compliance automation can cover many areas:

• Policy enforcement – Automating the implementation of rules and controls across systems and platforms
• Data monitoring – Continuously monitoring data to ensure compliance with legislation and procedures
• Alerting – Sending automatic alerts when possible violations are found and starting remediation workflows
• Reporting – Automated creation of compliance reports for audits and reviews.
• Risk assessment – Identifying and assessing compliance-related risks with no extra workload

Compliance automation technologies interact with multiple systems and platforms used in HR, security, and cloud services. The tooling continuously watches for policy infractions and automatically responds. And real-time reporting ensures compliance status and creates audit-ready results.

Promote Only Validated Datasets to Production

Implement a rigorous data validation procedure that includes tests for data quality, consistency, and relevance to the production environment. This ensures that only valid and trustworthy data is utilized to train and deploy models, reducing the likelihood of errors and poor performance in the live system.

The first step is adding data quality checks to your process. These are rules and limits for data completeness (no missing values), accuracy (data is correct), consistency (data conforms to specified formats and ranges), and uniqueness (no duplicate entries). Schema validation ensures that data follows the anticipated structure and types.

Automate validation by creating scripts or using specialized data validation libraries or AI frameworks. Make sure to validate data as it gets integrated from multiple sources to ensure consistency and avoid errors during the ETL (Extract, Transform, Load) process.

Integrate Metadata Management and Cataloging

Integrating metadata management and data cataloging is how you build a strong data management system. Data catalogs make it easier to identify and access data, while metadata management provides helpful data definitions, lineage, quality scores, and ownership.

Metadata serves as the cornerstone of a data catalog, enabling discoverability and useful data. Metadata management systems ensure correct and consistent information in data catalogs, aligning with business policies. This is why integrating metadata management and data cataloging improves data governance procedures, assuring quality, security, and compliance.

Common Challenges in Achieving AI-Ready Data

Challenge	Description
Siloed Data Across Tools and Teams	If you store data in several locations, you may wind up with silos. Data silos often reflect your corporate structure, with each department having a unique approach to data. When your team is obtaining data to train the model, they may struggle to find the ‘real’ data hidden within those silos. The more fragmented your data, the more challenging it becomes to establish connections and provide the right dataset to your AI application. To train an AI model, build a data platform that connects all data silos and sources relevant data.
Lack of Visibility into Data Lineage	A lack of insight into data lineage, or the path that data takes as it moves and transforms within a system, can result in erroneous reporting, compliance issues, and difficulty understanding how data is used. This lack of insight makes efficient data management difficult, particularly in large, dispersed systems.
Duplicate Data	You might have identical data obtained from one or more channels or using two tools that record the same data, resulting in unnecessary clutter. Consider the data format, usage, and quality level to overcome AI data issues and keep the dataset clean. Use algorithmic approaches to perform exact matches and remove duplicates. When the situation is not as straightforward, you can utilize a fuzzy match or even train special AI models to resolve the duplicates.
Downgrades for Outdated Data Relevance	Data has an expiry date. Using out-of-date data sets can reduce the quality and relevance of your AI data. To maintain accurate and up-to-date data, schedule regular cleaning and update sessions. Use data versioning to automatically track changes to your data as well.
Lack of Reproducibility	This issue impedes researchers’ ability to verify discoveries and expand on previous work, eventually impeding scientific advancement. As your data volume grows, so do the possible data-related key challenges. Inaccurate implementations can jeopardize the model’s performance and your ROI in AI. The type of data collected, how it is stored, and whether it is clean determine these potential data discrepancies.

Best Practices for Managing Data in AI/ML Pipelines

Version Both Data and Metadata for Full Reproducibility

As ML and AI systems become more sophisticated, teams should pay equal attention to managing data as well as metadata. Metadata may be particularly tricky, as the vast amount of metadata from various data sources makes tracking and organizing challenging, and inconsistent metadata patterns make integration and querying between sources difficult.

What you need is a solution that supports various data types (such as tables, views, streams, document collections, and dashboards) and can handle large-scale data lakes at the object level, allowing for object-level metadata. It should be format-independent and adaptable to any dataset structure.

Use Branching to Safely Test Changes and New Models 

Branching is an essential strategy for securely testing both changes and brand-new models. It’s all about creating distinct, isolated environments (branches) in which team members may apply changes without affecting the main/production dataset. This lets them experiment, test, and revise changes before committing them to the main branch, reducing the chance of introducing problems or damaging current functionality.

Branching reduces risk by isolating changes and preventing their impact on the main branch, letting multiple team members work on different features or experiments simultaneously without interference. A branching mechanism tied to a data versioning solution allows for easy tracking of changes and reverting to earlier versions.

Make Data Easily Discoverable with Rich Metadata

Adding rich metadata pays off in the long run. Fill in your metadata fields with extensive descriptions, pertinent tags, and keywords to improve the discoverability of your data. Rich metadata also helps in identifying and addressing biases, debugging edge cases, and uncovering gaps in our training data. 

Track Data and Pipeline Changes End-to-End

Do you want to ensure data integrity, performance, and efficient issue resolution? You’ll need the capability of tracking data and pipeline changes end-to-end, monitoring the full data flow lifetime, from origin to destination. This includes tracking data quality, pipeline performance, and data lineage at every level.

Performance monitoring tools come in handy here – they analyze system-level parameters such as CPU usage, network bandwidth, and memory consumption to provide insight into infrastructure health.

Automate as much of the pipeline process as feasible to increase efficiency and minimize manual labor. Automated processes can handle massive amounts of data quickly, allowing for more timely, data-driven decisions.

Support Parallel Experimentation Across Teams

Change the way your ML teams interact with data by enabling collaboration, reproducible outcomes, and rapid iterative experimentation. Set up experimentation environments using branching and find a solution that lets you run concurrent experiments without duplicating terabytes of data. 

Make sure that your team can confidently repeat experiments, track the progression of datasets with model changes, and safely share datasets across teams.

Leverage Immutable Snapshots for Debugging and Audits 

Immutable snapshots are read-only copies of data taken at a certain point in time. Once created, these snapshots cannot be changed, ensuring that the data remains in its original state. This feature separates them from mutable snapshots, which can be modified after creation.

Immutable snapshots are critical for maintaining data integrity and authenticity. They ensure that data may be safely accessed for future reference without the risk of alteration. They also contribute to data protection techniques by providing a consistent record of data at specific times.

Enable Rollback Capabilities to Quickly Recover from Errors

A rollback operation helps teams rectify critical data problems right away. For example, incorrect or misformatted data may cause a substantial problem with an essential service. In such a scenario, the priority is to stop the bleeding.

If you have rollback capabilities, this might mean that rolling back data will return it to a previous state before the problem occurred. Although you may not show all of the most recent data after a rollback, you won’t be displaying wrong data or generating errors.

Assessing AI Data Readiness in Your Organization

To ensure AI success, enhance your data preparation process with an emphasis on data lineage, metadata, and governance. This includes detecting gaps, prioritizing improvements, aligning data with model objectives, and training teams on data debt.

Use a Checklist to Identify Gaps (Lineage, Metadata, Governance)

Here’s an example checklist:

• Understand the origin and transformations of your data. Determine where data comes from, how it moves through systems, and what changes occur.
• Ensure data is clearly documented and comprehensible. This encompasses descriptive, structural, administrative, reference, statistical, and legal metadata.
• Establish data governance policies and procedures for data quality, access, and usage. Evaluate the accuracy, completeness, and consistency of data from many sources.
• Assess AI systems’ ability to quickly access relevant data from several sources.
• Maintain data security and privacy by storing and using it securely and in compliance.

Prioritize and Sequence Improvements

To improve AI model performance and achieve business objectives, prioritize areas with the most impact. Break down data silos to make data more accessible and usable across the enterprise. The iterative approach involves implementing changes in stages, beginning with trial projects and eventually scaling them.

Align Data Readiness with Model Objectives

Make sure that the data is appropriate for the AI models and applications being built. Regularly review and align data readiness with model objectives as AI initiatives grow.

And don’t forget that preparing data for AI systems involves cleansing, transformation, and feature engineering.

Educate Teams on the Impact of Data Debt

Explain what data debt is and emphasize the repercussions of poor data quality, showing how it affects AI model performance. Promote data literacy by training teams on data governance, metadata, and quality procedures. And make sure to encourage a data-driven culture by prioritizing data quality and proactively addressing concerns related to it.

How to Foster AI-Ready Data Practices

Best Practice	Description
Encourage Ownership of Data Quality	To develop a data-driven culture and ensure high-quality data, instill a sense of ownership around the quality of your data. This includes explicitly defining roles, offering training, encouraging collaboration, and recognizing efforts to improve data quality.
Align Data and ML Engineering Teams	To connect data and ML engineering teams, prioritize clear communication, shared goals, and collaborative workflows. Create a single language, clarify roles and duties, and cultivate a culture of mutual understanding through workshops and frequent meetings. This guarantees that everyone is working toward the same goals, optimizing resource allocation and increasing the effect of ML projects.
Integrate Version Control into Everyday Workflows	Version control workflows are systematic processes used to track changes to files, codebases, or projects over time. At its heart, version control is a system that saves changes to a file or set of files, allowing users to access specific versions later.
Promote Transparency in Data Changes and Approvals	Data transparency is more than simply making data available; it’s the practice of making data intelligible and easily accessible to anyone with a legitimate interest in it.

How lakeFS Helps You Build AI-Ready Data

lakeFS is an open-source system that lets teams manage their data using Git-like operations (commit, merge, etc.) while scaling to billions of files and petabytes of data. It adds a management layer to any S3-compatible object store, allowing you to manage your data within that bucket like code by providing version control capabilities.

Isolation is a key aspect of source control. Just as Git provides isolation for code development through feature branches, lakeFS brings this crucial capability to data. This solves a major pain point for data practitioners: the difficulty and cost of creating isolated copies of large datasets for experimentation. With lakeFS, you eliminate the need for expensive data replication to achieve isolated workspaces.

Multiple team members can work on the same data concurrently, each creating a separate branch for their experiments. You can also tag data to represent specific experimental states, ensuring accurate and reproducible results.

Once the change works, you can push or merge it back into the main branch for customers. Alternatively, you may instantly revert changes without having to go through each file individually, like with S3. You can revert the change and return to the last good condition.

Check out the full example in this article: How To Improve ML Pipeline Development With Reproducibility

Conclusion

AI-ready data is a fundamental advantage for any organization that wants to tap into AI’s potential in a scalable and safe way. Developing processes and tooling is essential to ensure your data is structured, consistent, and rich in metadata. Otherwise, improving model accuracy and streamlining MLOps processes will remain out of reach.